A comparison across clinics and between pilgrim and peripheral sites

The aim of this project is to audit our use of general surgery and colorectal surgery clinics. We acquired our clinic attendance data from hospital information services. We further analysed this data to assess our utilization and DNAs. These are the clinic codes use for the purpose of this analysis JH-MIRO2, JH-ZAIO4, PH-ATE35, PH-ATEPF, PH-GOR52, PH-MIR41, PH-MIRPF, PH-MMC35, PH-MOB21, PH-MOB40, PH-RTH11, PH-RTH45, PH-ZAI50, SD-MMCSD, SD-ATESD

1.The Data

With a preliminary view we can see that our Pilgrim Median Booking Rate:91.7% is marginally higher than our Peripheral Median Booking Rate:89.5%. Difference in median was found to be statistically significant at p-value of 0.023(Graph 1.1A). Similarly our Pilgrim Median Utilizaton Rate:83.3% is marginally higher than our Peripheral Median Utilization Rate:79.2%. Difference in median was found to be statistically significant at p-value of 0.011(Graph 1.1B)

Graph 1.1

Table-1: Total number of One and Two Man Clinics Per Month for Pilgrim and Peripheral Sites

Graph-1.2 Histogram demonstrating the distribution of Clinic Utilizatation Rates

2.Further Breakdown

The following graphs demonstrate per clinic data. Black Shapes demonstrate booking rate while Red Shapes demonstrate utilization rates. These are monthly rates ie the actual figure is an average of clinics used per month. Booking rate is \[\frac{initial\ booked slots}{total\ available\ slots}\] while Utilization Rate is \[\frac{attended \ clinic\ slots}{booked\ slots}\]

Graph-2.1 Booking and Utilization rate per month for One Man clinics

Graph-2.2 Booking and Utilization Rates per month for Two Man clinics

Graph-2.3 Utilization and Booking Rate per month across Peripheral vs Pilgrim clinics

### Graph-2.4 Utilization and Booking Rate per month across Peripheral vs Pilgrim clinics

3.Discussion

Graph-2.4 & Graph-2.3 demonstrates again underutilized peripheral clinics clinics(p-value:0.011) despite having similar Booking Rates. This appears to be more during certain month. Again however due the small sample sizes it is not possible to perform adequate analysis.

Initially it seems that the differences although statistically significant were small. However when consulting the last 4 charts it seems evident that a notable number of our clinics were underbooked at 80% booking rate(which translates to 2 clinic slots for 1-man-clinics and about 3 clinic slots for 2-man-clinics). Although those numbers warrant attention their statistical significance is not easily demonstrated due to the small sample sizes(As shown on Table 1.1). If we were to assume their significance the next question we need to answer is why?.

And Finally the next pertinent question is do we that small of a population to explain our underutilization?

4.Recommendations

We suggest the following recommendations

Create waiting list Allocate Clinic Space by proximity Monthly Review of Booking Rate
Improve Communication Between Staff accross sites *Disseminate Booking Rate to all concerned Staff